


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase__			:      Dict								=8






def 						a__			(				A__,      A__=BITS				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					x.device

			SCREAMING_SNAKE_CASE_		:     Dict					=					(x * 2_5_5).int().clamp(0,      2_5_5				)

			SCREAMING_SNAKE_CASE_		:     str					=					2 ** torch.arange(bits - 1,      -1,      -1,      device=__lowerCAmelCase				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rearrange(__lowerCAmelCase,      'd -> d 1 1'				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rearrange(__lowerCAmelCase,      'b c h w -> b c 1 h w'				)

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					((x & mask) != 0).float()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					rearrange(__lowerCAmelCase,      'b c d h w -> b (c d) h w'				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					bits * 2 - 1
			return bits






def 						a__			(				A__,      A__=BITS				):
			SCREAMING_SNAKE_CASE_		:     str					=					x.device

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(x > 0).int()
			SCREAMING_SNAKE_CASE_		:     Any					=					2 ** torch.arange(bits - 1,      -1,      -1,      device=__lowerCAmelCase,      dtype=torch.intaa				)

			SCREAMING_SNAKE_CASE_		:     List[str]					=					rearrange(__lowerCAmelCase,      'd -> d 1 1'				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rearrange(__lowerCAmelCase,      'b (c d) h w -> b c d h w',      d=8				)
			SCREAMING_SNAKE_CASE_		:     str					=					reduce(x * mask,      'b c d h w -> b c h w',      'sum'				)
			return (dec / 2_5_5).clamp(0.0,      1.0				)






def 						a__			(				self,      A__,      A__,      A__,      A__ = 0.0,      A__ = True,      A__=None,      A__ = True,      ):



			if self.num_inference_steps is None:
						raise ValueError(
						    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'				)

			# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
			# Ideally, read DDIM paper in-detail understanding

			# Notation (<variable name> -> <name in paper>
			# - pred_noise_t -> e_theta(x_t, t)
			# - pred_original_sample -> f_theta(x_t, t) or x_0
			# - std_dev_t -> sigma_t
			# - eta -> η
			# - pred_sample_direction -> "direction pointing to x_t"
			# - pred_prev_sample -> "x_t-1"

			# 1. get previous step value (=t-1)
			SCREAMING_SNAKE_CASE_		:     Any					=					timestep - self.config.num_train_timesteps // self.num_inference_steps

			# 2. compute alphas, betas
			SCREAMING_SNAKE_CASE_		:     Any					=					self.alphas_cumprod[timestep]
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					1 - alpha_prod_t

			# 3. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

			# 4. Clip "predicted x_0"
			SCREAMING_SNAKE_CASE_		:     Any					=					self.bit_scale
			if self.config.clip_sample:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(__lowerCAmelCase,      -scale,      __lowerCAmelCase				)

			# 5. compute variance: "sigma_t(η)" -> see formula (16)
			# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					self._get_variance(__lowerCAmelCase,      __lowerCAmelCase				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						SCREAMING_SNAKE_CASE_		:     int					=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

			# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

			# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			SCREAMING_SNAKE_CASE_		:     Any					=					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

			if eta > 0:
						# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
						SCREAMING_SNAKE_CASE_		:     str					=					model_output.device if torch.is_tensor(__lowerCAmelCase				) else 'cpu'
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.randn(model_output.shape,      dtype=model_output.dtype,      generator=__lowerCAmelCase				).to(__lowerCAmelCase				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					self._get_variance(__lowerCAmelCase,      __lowerCAmelCase				) ** 0.5 * eta * noise

						SCREAMING_SNAKE_CASE_		:     Any					=					prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase,      pred_original_sample=__lowerCAmelCase				)






def 						a__			(				self,      A__,      A__,      A__,      A__="epsilon",      A__=None,      A__ = True,      ):
			SCREAMING_SNAKE_CASE_		:     Any					=					timestep

			if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.split(__lowerCAmelCase,      sample.shape[1],      dim=1				)
			else:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

			# 1. compute alphas, betas
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.alphas_cumprod[t]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					self.alphas_cumprod[t - 1] if t > 0 else self.one
			SCREAMING_SNAKE_CASE_		:     Any					=					1 - alpha_prod_t
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1 - alpha_prod_t_prev

			# 2. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
			if prediction_type == "epsilon":
						SCREAMING_SNAKE_CASE_		:     List[str]					=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif prediction_type == "sample":
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model_output
			else:
						raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''				)

			# 3. Clip "predicted x_0"
			SCREAMING_SNAKE_CASE_		:     List[str]					=					self.bit_scale
			if self.config.clip_sample:
						SCREAMING_SNAKE_CASE_		:     int					=					torch.clamp(__lowerCAmelCase,      -scale,      __lowerCAmelCase				)

			# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
			# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			SCREAMING_SNAKE_CASE_		:     Tuple					=					self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

			# 5. Compute predicted previous sample µ_t
			# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

			# 6. Add noise
			SCREAMING_SNAKE_CASE_		:     int					=					0
			if t > 0:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.randn(
						    model_output.size(),      dtype=model_output.dtype,      layout=model_output.layout,      generator=__lowerCAmelCase				).to(model_output.device				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					(self._get_variance(__lowerCAmelCase,      predicted_variance=__lowerCAmelCase				) ** 0.5) * noise

			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase,      pred_original_sample=__lowerCAmelCase				)







class 	__lowercase    (__lowerCAmelCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = 1.0			,						):





										"""simple docstring"""




										super().__init__()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					bit_scale
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
										    ddim_bit_scheduler_step if isinstance(_lowerCamelCase			,						_lowerCamelCase       ) else ddpm_bit_scheduler_step
										)

										self.register_modules(unet=_lowerCamelCase			,						scheduler=_lowerCamelCase       )






							@torch.no_grad()
							def __call__(      self			,						lowerCAmelCase__ = 2_5_6			,						lowerCAmelCase__ = 2_5_6			,						lowerCAmelCase__ = 5_0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 1			,						lowerCAmelCase__ = "pil"			,						lowerCAmelCase__ = True			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.randn(
										    (batch_size, self.unet.config.in_channels, height, width)			,						generator=_lowerCamelCase			,						)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					decimal_to_bits(_lowerCamelCase       ) * self.bit_scale
										SCREAMING_SNAKE_CASE_		:     int					=					latents.to(self.device       )

										self.scheduler.set_timesteps(_lowerCamelCase       )

										for t in self.progress_bar(self.scheduler.timesteps       ):
													# predict the noise residual
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.unet(_lowerCamelCase			,						_lowerCamelCase       ).sample

													# compute the previous noisy sample x_t -> x_t-1
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.scheduler.step(_lowerCamelCase			,						_lowerCamelCase			,						_lowerCamelCase       ).prev_sample

										SCREAMING_SNAKE_CASE_		:     List[str]					=					bits_to_decimal(_lowerCamelCase       )

										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.numpy_to_pil(_lowerCamelCase       )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=_lowerCamelCase       )






"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[Any]    				=			'''Wav2Vec2FeatureExtractor'''
				A__ :  Any    				=			'''AutoTokenizer'''

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										try:
																return super().from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
										except OSError:
																warnings.warn(
																    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																    " include a `tokenizer_class` attribute is deprecated and will be "
																    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																    " attribute to either your `config.json` or `tokenizer_config.json` "
																    "file to suppress this warning: "      ,					_lowerCamelCase      ,					)

																lowerCamelCase__   =WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
																lowerCamelCase__   =WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )

																return cls(feature_extractor=_lowerCamelCase      ,					tokenizer=_lowerCamelCase )

				def __call__( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*_lowerCamelCase      ,					**_lowerCamelCase )

										if "raw_speech" in kwargs:
																warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
																lowerCamelCase__   =kwargs.pop("raw_speech" )
										else:
																lowerCamelCase__   =kwargs.pop("audio"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("sampling_rate"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("text"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if audio is None and text is None:
																raise ValueError("You need to specify either an `audio` or `text` input to process." )

										if audio is not None:
																lowerCamelCase__   =self.feature_extractor(_lowerCamelCase      ,					*_lowerCamelCase      ,					sampling_rate=_lowerCamelCase      ,					**_lowerCamelCase )
										if text is not None:
																lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					**_lowerCamelCase )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																lowerCamelCase__   =encodings["input_ids"]
																return inputs

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor.pad(*_lowerCamelCase      ,					**_lowerCamelCase )

										lowerCamelCase__   =kwargs.pop("input_features"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("labels"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if input_features is not None:
																lowerCamelCase__   =self.feature_extractor.pad(_lowerCamelCase      ,					*_lowerCamelCase      ,					**_lowerCamelCase )
										if labels is not None:
																lowerCamelCase__   =self.tokenizer.pad(_lowerCamelCase      ,					**_lowerCamelCase )

										if labels is None:
																return input_features
										elif input_features is None:
																return labels
										else:
																lowerCamelCase__   =labels["input_ids"]
																return input_features

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.decode(*_lowerCamelCase      ,					**_lowerCamelCase )






				@contextmanager
				def 						_a      ( self ):
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call." )
										lowerCamelCase__   =True
										lowerCamelCase__   =self.tokenizer
										yield
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False


